A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling

Tuttle, Jacob F.; Blackburn, Landen D.; Andersson, Klas; Powell, Kody M.

doi:10.1016/j.apenergy.2021.116886

A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling

Journal Article · Sun Apr 18 00:00:00 EDT 2021 · Applied Energy

DOI:https://doi.org/10.1016/j.apenergy.2021.116886· OSTI ID:1849308

Tuttle, Jacob F. ^[1]; Blackburn, Landen D. ^[2]; Andersson, Klas ^[3]; Powell, Kody M. ^[2]

Univ. of Utah, Salt Lake City, UT (United States); OSTI
Univ. of Utah, Salt Lake City, UT (United States)
Chalmers University of Technology, Gothenburg (Sweden)

Ten established, data-driven dynamic algorithms are surveyed and a practical guide for understanding these methods generated. Existing Python programming packages for implementing each algorithm are acknowledged, and the model equations necessary for prediction are presented. A case study on a coal-fired power plant’s NO_x emission rates is performed, directly comparing each modeling method’s performance on a mutual system. Each model is evaluated by its root mean squared error (RMSE) on out-of-sample future horizon predictions. Optimal hyperparameters are identified using either an exhaustive search or genetic algorithm. The top five model structures of each method are used to recursively predict future NO_x emission rates over a 60-step time horizon. The RMSE at each future timestep is determined, and the recursive output prediction trends compared against measurements in time. The GRU neural network is identified as the best candidate for representing the system, demonstrating accurate and stable predictions across the future horizon by all considered models, while satisfactory performance was observed in several of the ARX/NARX formulations. Finally, these efforts have contributed 1) a concise resource of multiple proven dynamic machine learning methods, 2) a practical guide explaining the use of these methods, effectively lowering the “barrier-to-entry” of deploying such models in control systems, 3) a comparison study evaluating each method’s performance on a mutual system, 4) demonstration of accurate multi-timestep emissions modeling suitable for systems-level control, and 5) generalizable results demonstrating the suitability of each method for prediction over a multi-step future horizon to other complex dynamic systems.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Utah, Salt Lake City, UT (United States)

Sponsoring Organization:: USDOE Office of Fossil Energy (FE)

Grant/Contract Number:: FE0031754

OSTI ID:: 1849308

Journal Information:: Applied Energy, Journal Name: Applied Energy Journal Issue: C Vol. 292; ISSN 0306-2619

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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